A majority of US employer-sponsored plans use copay tiers for prescription drugs to control healthcare expenditures. These copay tiers are generally determined based on the cost of the drugs rather than the value provided by the drugs. However, setting copays based on cost and not value can lead to inefficient use of drugs. A value-based formulary (VBF) is a development in designing prescription drug benefits in which the value of each medication is estimated using cost-effectiveness analysis (CEA). The explicit use of CEA to guide copay tier placement differs from previous value-based insurance designs (VBIDs) which have uniformly reduced copays for all drugs within a therapeutic area, designating the therapeutic area to be of high value, even though not all medications within a therapeutic area have the same value. In contrast, the VBF uses CEA to estimate the value of each individual drug. Copays can then be assigned based on individual drug value rather than by therapeutic class. Furthermore, this method can define low value drugs (previous VBIDs have only defined high value therapeutic areas) and increase their corresponding copays. While promising, the impact of the VBF cannot be predicted and requires empirical evaluation. First, CEA estimates were drawn from a wide range of sources and the validity of such estimates is uncertain. Second, the effect of copay changes based on population average estimates of value can have unpredictable effects on the marginal choice of medication. In 2010, Premera Blue Cross, a large non-profit health plan in the Pacific Northwest, implemented a VBF. We exploit this natural experiment to evaluate the impact of a VBF as implemented by a commercial health insurance plan. Furthermore, the changes in copay for all drugs in the VBF provide a unique opportunity to explore cross- price elasticities of demand among individuals with multiple chronic conditions. Almost a third of Americans have 2 or more chronic conditions and these individuals account for about 71% of national health expenditures. Yet, adherence rates for medications used to treat many chronic conditions are suboptimal-ranging between 35%- 72%. Evaluation of cross-price elasticities would complement our understanding of own-price elasticities for these individuals. This study will utilize an interrupted time series design with concurrent control grou in order to examine the following 3 aims: (1) assess the impact of the VBF on medication utilization (2) assess the impact of the VBF on both member out of pocket costs and total healthcare costs and (3) estimate cross-price elasticities of demand for individuals with multiple chronic conditions. In order to accomplish these aims, we propose to use the following 3 methods with segmented regression models: (1) two-part model with quantile regression (2) extended estimating equations (3) alternative-specific conditional logistic regression.